This paper presents an unified framework for fast interactive segmentation of natural images using the image foresting transform (1FT) - A tool for the design of image processing operators based on connectivity functions (path-value functions) in graphs derived from the image. It mainly consists of three tasks: recognition, enhancement, and extraction. Recognition is the only interactive task, where representative image properties for enhancement and the object's location for extraction are indicated by drawing a few markers in the image. Enhancement increases the dissimilarities between object and background for more effective object extraction, which completes segmentation. We show through extensive experiments that, by exploiting the synergism between user and computer for recognition and enhancement, respectively, as a separated step from recognition and extraction, respectively, one can reduce user involvement with better accuracy. We also describe new methods for enhancement based on fuzzy classification by 1FT and for feature selection and/or combination by genetic programming. © 2009 IEEE. Martin, D., Fowlkes, C., Tal, D., Malik, J., Berkeley segmentation dataset and bench-mark, , http://www.eecs.berkeley.edu/Research/Projects/CS/vision/groupingRother, C., Kolmogorov, V., Blake, A., Brown, M., Image and Video Editing: Grabcut, , http://research.microsoft.com/en-us/um/cambridge/projects/ visionimagevideoediting/segmentation/grabcut.htmFalcão, A.X., Udupa, J.K., Miyazawa, F.K., An ultrafast user-steered image segmentation paradigm: Livewire-on-the-fly (2000) IEEE Trans. on Medical Imaging, 19 (1), pp. 55-62Boykov, Y.Y., Jolly, M.-P., Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images (2001) International Conference on Computer Vision (ICCV), 1, pp. 105-112Blake, A., Rother, C., Brown, M., Perez, P., Torr, P., Interactive image segmentation using an adaptive gmmrfmodel (2004) 8th European Conf. on Computer Vision (ECCV), 1, pp. 428-441. , MayRother, C., Kolmogorov, Y., Blake, A., "Grabcut": Interactive foreground extraction using iterated graph cuts (2004) ACM Transactions on Graphics, 23 (3), pp. 309-314Protiere, A., Sapiro, G., Interactive image segmentation via adaptive weighted distances (2007) IEEE Transactions on Image Processing, 16 (4), p. 10461057. , AprBai, X., Sapiro, G., A geodesic framework for fast interactive image and video segmentation and matting (2007) ICCV (International Conference on Computer Vision), pp. 1-8. , Rio De Janeiro, Brazil, IEEEVicente, S., Kolrnogorov, Y., Rother, C., Graph cut based image segmentation with connectivity priors (2008) IEEE Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 1-8. , Anchorage, Alaska, JunFalcão, A.X., Stolfi, J., Lotufo, R.A., The image foresting transform: Theory, algorithms, and applications (2004) IEEE Trans. on Pattern Analysis and Machine Intelligence, 26 (1), pp. 19-29Falcão, A.X., Bergo, F.P.G., Interactive volume segmentation with differential image foresting transforms (2004) IEEE Trans. on Medical Imaging, 23 (9), pp. 1100-1108Falcão, A.X., Bergo, F.P.G., Miranda, P.A.Y., Image segmentation by tree pruning (2004) XV/f Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPl), pp. 65-71. , Oct, IEEEBergo, F.P.G., Falcão, A.X., Miranda, P.A.Y., Rocha, L.M., Automatic image segmentation by tree pruning (2007) Journal ofMathematical Imaging and Vision, 29 (2), pp. 141-162. , NovMiranda, P.A.Y., Falcão, A.X., Rocha, A., Bergo, F.P.G., Object delineation by x-connected components (2008) EURASIP Journal on Advances in Signal Processing, pp. 1-14. , doi: 10.1155/2008 /467928Banzhaf, W., Nordin, P., Keller, R.E., Francone, F.D., (1998) Genetic Programming - An IntroductionOn the Automatic Evolution ofComputer Programs and its Applications, , Morgan Kaufmann, San Francisco, CA, USA, JanPortilla, J., Simoncelli, E.P., A parametric texture model based on joint statistics of complex wavelet coefficients (2000) IntI. Journal ofComputer Vision, 40 (1), pp. 49-70Meyer, F., Levelings, image simplifi cation filters for segmentation (2004) Journal of Mathemati cal Imaging and Vision, 20 (1), pp. 59-72Cormen, T., Leiserson, C., Rivest, R., (1990) Introduction to Algorithms, , MITPapa, J.P., Falcão, A.X., Suzuki, C.T.N., Mascarenhas, N.D.A., A discrete approach for supervised pattern recognition (2008) Proc. of the 12th Inti. Workshop on Combinatorial Image Analysis, LNCS 4958, pp. 136-147. , Buffalo, NY, USA, Apr 7th-9th SpringerTorres, R.S., Falcão, A.X., Gon Calves, M.A., Papa, J.P., Zhang, B., Fan, W., Fox, E.A., A genetic programming framework for content-based image retrieval (2009) Pattern Recognition, 42, pp. 283-292. , FebruaryVan Rijsbergen, C.J., (1979) Information Retrieval, , Wiley Interscience,London, second edition